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Multicast Device-to-Device Communication underlaying WPCNs
Allu Sai Kumar1, A. Mallikarjuna Prasad2
1Department
of Electronics and Communication Engineering, University College of engineering, Kakinada, India
Department of Electronics and Communication Engineering, University College of engineering
Kakinada, India
---------------------------------------------------------------------***--------------------------------------------------------------------2Professor,
Abstract - In this paper, we maximize the sum throughput
via joint time scheduling and power control
of device-tomulti-device (D2MD) wireless communications. We
investigate the resource allocation problem for
D2D
communications
underlaying
wireless
powered
communication networks (WPCNs), where multiple D2D
pairs harvest energy from a power station equipped with
multiple antennas and then transmit information signals
simultaneously over the same spectrum resource. The use of
multicast communications opens the possibility of reusing
the spectrum resources also inside the groups. The
optimization problem is formulated as a mixed integer
nonlinear joint optimization for the power control and
allocation of resource blocks (RBs) to each group. The aim is
to maximize the sum throughput of D2Dmulticast groups
through resource allocation and power control scheme,
which considers the quality-of-service (QoS) requirements of
both cellular user equipment and D2D groups. Simulation
results demonstrate that the proposed scheme works well in
different scenarios.
Key Words:
Device-to-Device (D2D) Multicast,
Resource
Allocation,
Cellular
network,
sum
throughput maximization, power consumption
retransmission.
1. INTRODUCTION
Simultaneous wireless information and power transfer
(SWIPT) refers to using the same emitted electromagnetic
(EM) wave field to transport both energy that is harvested
at the receiver, and information that is decoded by the
receiver. The dual use of radio frequency signals,
wireless energy transfer (WET) has attracted much
attention for improving the system energy efficiency
[1][2].
In this context, simultaneous wireless information and
power transfer (SWIPT) and wireless powered
communication networks (WPCNs) have been extensively
studied in the literature. Moreover, since electromagnetic
waves decay quickly over distance, energy beamforming is
generally designed to achieve efficient WET. In WPCNs, a
power station (PS) transfers wireless energy to some lowpower users with a single antenna due to the hardware
constraint [3]. Afterwards, the users transmit information
signals with the harvested energy. For the multiple users
scenario, the signals are transmitted typically based on
time division multiple access (TDMA) as in [5]. However,
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the spectrum efficiency can be greatly improved with
appropriate interference management methods by
allowing
multiple
users
to
transmit
signals
simultaneously.
The demand for multimedia data is increasing rapidly
now-a-days, which has led to the insufficiency of available
spectrum resource [4]. The continuously increasing
demand for wireless access especially data services for
geographically proximate user has brought great
challenges to current
mobile communication
infrastructure. Under this circumstance, the direct
connectivity between mobile devices, namely, Device-toDevice (D2D) communication emerges as a key
component for the fifth generation (5G) wireless
communication system [5]. D2D communication is a kind
of close range data transmission over a direct link which
coexists with cellular networks. It increases the total
throughput of a cell with limited interference impacts on
the primary cellular network. Furthermore, the D2D
communication has potential at saving the power and
reducing delay. Assume modern smart phones and tablet
PC have sufficient storage capacity and capable battery,
they can transmit multimedia data to the users nearby in
D2D multicast communication.
A further improvement of D2D is to allow individual
devices to form clusters where one device can broadcast
information to multiple receivers. Multicasting through
D2D (MD2D) [8] communication is appealing when the
same data is requested by multiple devices in restricted
geographical areas. By leveraging the multicast nature of
wireless communications, MD2D technology delivers the
shared content to multiple users simultaneously. In
particular, as the traffic of local content sharing grows
rapidly, MD2D scenarios are reasonably possible.
However, the MD2D implementation leads to a more
challenging and complex control problem in real-world
cellular network operations.
In D2D communication, user equipment (UE) [6] in the
proximity region transmits data signals to each other over
a direct link instead of through the base station (BS),
which improves spectral utilization and saves power
consumption. As one of the main features, D2D multicast
transmission provides an effective solution to mitigate the
burden of BS and improves transmission efficiency, which
is important for scenarios such as content sharing, device
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discovery,
and
public
safety.
communication underlaying cellular.
D2D
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multicast
of the regular cellular users, effective interference
management strategy is indispensable so as to deal with
complex resource reuse interference. Hence, how to
energy-efficiently allocate spectrum resource and power
for D2D multicast groups is also a big challenge.
2. SYSTEM MODEL
This work focuses on the reliable multicast of common
data from BS in a cell of cellular network to N user devices
which are close to one another, forming a D2D-MC [8], as
shown in Fig. 2.
Fig 1.A network model of wireless powered
communication
networks refers to the transmission scenario that a
transmitter, namely, cluster head(CH), sends the same
packet to a group of destination UE items over the D2D
links which reuse the resource of cellular links. By
exploiting the inherent broadcast nature of wireless
channels, [9] D2D multicast transmission disseminates the
same content simultaneously to multiple recipients.
Compared with traditional point-to-point unicast D2D
communication, multicast D2D transmission reduces
overhead and improves resource efficiency. However,
multicast D2D transmission underlaying cellular network
also has its own challenges.
The first challenge faces with D2D multicast
transmission are how to energy-efficiently form a specific
multicast group and how to select an appropriate CH for
each multicast group. Due to the increasing power
consumption of information and communication
technology (ICT) industry, the progress in battery
technology of user terminals is rather slow. Given the
limited battery capacity of mobile devices, energy-efficient
solutions are imperative to be integrated into the D2D [9]
communications. On the other hand, recent studies show
that the social behaviors of human beings who carry the
handheld communication devices can be leveraged to
improve the performance of D2D communications.
Consequently, it is worth discussing whether the
knowledge of social characteristics can benefit the quality
of service (QoS) of D2D multicast communication.
In a multicast group, the situation is especially
undesirable when most of the UE items are in good
channel condition and capable of high data transmission,
while only a small fraction of the UE items are suffering
from deep fading. Thus, how to allocate resource within a
multicast group so as to guarantee reliable QoS
requirement of different UE is very essential. Besides,
when D2D communication reuses the spectrum resource
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Fig 2. System model of MD2D Communication
In underlay D2D communication, cellular and D2D
communications share the same radio resources.
Consider a WPCN [7] with a PS equipped with K
antennas and N low-power MD2D pairs denoted by N= {1,
2, ..., n, ...,N}. The MD2D pair carries a single antenna due to
the size and cost constraints, such as the sensor node .
With no embedded energy supply, each MD2D-Tx first
harvests energy from wireless signal transmitted by the
PS(i.e., WET phase). Then, they utilize the harvested
energy to transmit information signals to their intended
receivers in the wireless information transmission (WIT)
phase. According to the harvest-then-transmit protocol, in
each block denoted by T, the first τ0T amount of time, 0 ≤
τ0 ≤1, is assigned to harvest energy for all D2D pairs,
while the followed τ1T amount of time in the same block is
assigned to transmit information signals. We consider a
normalized unit block time T = 1 in the sequel without loss
of generality. Then, there is τ0 + τ1 ≤ 1. All the users
considered in this paper operate on a single spectrum
band.
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In the WET phase, the K × 1 transmitted signal [8] is
given by p PS w, where p PS is the transmit power of the PS,
and the beamformer B is designed to improve the energy
transfer efficiency and subject to B = 1. Let hn represent
the M dimensional energy transfer channel vector
between the PS and n-th MD2D-Tx. The energy harvested
from the noise can be ignored since the noise power is
usually much smaller than that of the PS. Therefore, the
energy harvested at the n-th D2D-Tx is given by
w|2
En= ητ 0 p PS |
(1)
Denote gn,nas the channel power gain from the n-th
MD2D-Tx to its receiver. The channel power gain of the
interference link from the n-th MD2D-Tx to the k-th MD2D
receiver (D2D-Rx) is denoted by g˜n,k . Since all MD2D-Txs
transmit information signals simultaneously over the
same spectrum resource, the signal to interference plus
noise ratio at the n-th D2D-Rx is as follows:
=∑
(2)
(1 +
).
(3)
Due to this, a MD2D pair closer to the PS can harvest
more energy in short time and vice versa, which
potentially results in various energy constraints for
different MD2D pairs. The aim is to maximize the sum
throughput of all MD2D pairs via time scheduling and
power control, while satisfying the energy causality
constraints. Thus, the optimization problem can be
formulated as the following:
P1 :
(
*
∑
+
(
|
+ )



(
)
(
)
|
|
(5)
It is non-convex with respect to and , which hinders
the application of standard convex optimization
techniques. It can be observed that the non-convex
constraints are transformed into convex functions with a
tactful reformulation. Therefore, we try to search an
optimal solution to the problem (4) by solving the
equivalent problem. Denote q∗ as the optimal solution of
the considered problem (5),which is given by
∑
Set
(*
+)
(6)
The optimal solution is achieved if and only if
*
+
(
*
+)
∑
(*
+)- .
(7)
3. SIMULATION RESULTS
Where
is the transmit power of n-th D2D-Tx and
is the noise power. The achievable throughput at the n-th
receiver in bits/second/Hz is thus given by
=
words, the solution is not the optimal solution. The
constraint C1 in problem (4) can be transformed as
follows:
)
In this section, we perform in-depth simulations to
evaluate the performance of the proposed algorithm in a
50 × 50 m area, where multiple D2D pairs are randomly
located and the maximum distance between MD2D-Tx and
MD2D-Rx is d = 10 m and d = 8 m ,d is the distance
between the transmitter and receiver, and α = 3
represents the path-loss exponent. Unless specified
otherwise, the bandwidth is 1 MHz and noise power
spectral density is −170 dBm/Hz. The transmit power and
number of antennas are 5 W and 10 for the PS,
respectively. The energy conversion efficiency and circuit
power consumption are 0.5 and 0.1 μW. In all simulations,
q = 1 is set to start the algorithm and all results are
averaged over 100 realizations.
The following are the simulation results of multicast
D2D communication,
| ,
(4)
Where
represents the non-ideal circuit power
consumption (e.g., AC/DC converter, analog amplifier, and
processor). C1 guarantees that the consuming energy by
any D2D-Tx cannot exceed its harvested energy. C2, C3
and C4 are the time and power control constraints. Hence,
the optimization is possible by implementing the
Throughput maximization algorithm. The optimal solution
to the problem (4) is achieved if and only if all the time is
used, i.e., + = 1.
If we can find a feasible solution to the optimization
problem (4) in the remaining time, it demonstrates that
the system throughput can also be improved. In other
Fig 3. Sum throughput versus transmit power of the ps
at d=10m
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Table I-The sum throughput at various schemes
(based on distance)
Sum throughput
Settings
Distance
d=8m
Distance
d=10m
TDMA
12.5
9.32
OET
17.2
12.65
MULTICAST
D2D
21.5
15.01
Fig 6. Sum throughput versus circuit power
consumption at N=6
Table II-The sum throughput at various schemes
(based on number of md2d pairs)
Sum throughput
Fig 4. Sum throughput versus transmit
ps at d=8m
power of the
Settings
Number of
D2D
pairs
N=6
Number
of
D2D
pairs
N=3
TDMA
9.81
6.12
OET
8.23
6.15
MULTICAST D2D
16.01
10.20
Table III-Overall system sum throughput
Setting
Fig 5. Sum throughput versus circuit power
consumption at N=3
Number of antennas(M)=10
MD2D
No.of
MD2D
users
Circuit
power
consumption
(
w)
Circuit
power
consumption
(
w)
3
10.15
4.20
6
16.013
7.50
4. CONCLUSIONS
The sum throughput versus the transmit power of the
PS is shown in Fig. 3 & 4. For comparison, we also provide
an energy transfer scheme without beamforming, namely
the omnidirectional energy transfer (OET), and a TDMAbased algorithm where multiple users harvest energy and
then transmit information signals based on TDMA. It can
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be observed that the proposed algorithm outperforms the
OET and TDMA-based algorithm in all cases. Furthermore,
we can observe that the growth rate gradually becomes
slower as the transmit power increases. This is due to the
fact that the mutual interference among MD2D pairs
dominates the system with sufficiently large transmit
power. In addition, the final sum throughput would be
better if the maximum distance D between D2D-Tx and
D2D-Rx is reduced. The reason is that smaller maximum
distance results in better channel state.
The sum throughput is plotted against the circuit power
consumption
is shown in Fig 5 &6. It can be observed
that the sum throughput decreases with an increasing
circuit power consumption. Meanwhile, the throughput
gain between the proposed algorithm and the TDMAbased algorithm is smaller. The reason is that MD2D pairs
have little energy for information transmission and some
MD2D pairs may even stop working since they have not
enough energy.
The impact of number of antennas is further
investigated and simulation results are shown in Table I, II
& III. The plot confirms the intuition that the sum
throughput grows as more antennas are added at the PS
since more antennas can make use of the spatial resource
to improve diversity gain.
REFERENCES
[1] K. Huang and E. Larsson, “Simultaneous information
and power transfer for broadband wireless systems,” IEEE
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2013.
[2] S. Pejoski, Z. Hadzi-Velkov, T. Q. Duong, and C. Zhong,
“Wireless powered communication networks with nonideal circuit power consumption,” IEEE Commun. Lett., vol.
21, no. 6, pp. 1429–1432, Jun. 2017.
[3] H. Ju and R. Zhang, “Throughput maximization in
wireless powered communication networks,” IEEE Trans.
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[7] Haichao Wang , Jinlong Wang ,“D2D Communications
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august 2018.
[8]MariemHmila, Manuel Fernández-Veiga, “Energy
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BIOGRAPHIES
“Allu Sai Kumar received B.Tech degree
from Indira Institute of Technology and
Sciences which is affiliated to Jawaharlal
Nehru
Technological
University
Kakinada in Electrical and Electronics
Engineering. At present, Persuing M.Tech
in University College of Engineering
JNTUniversity, Kakinada in Computers
and Communications specialization”.
“A. Mallikarjuna Prasad did his B.Tech in
ECE from Nagarjuna University, during
1984-88. He did his M.Tech in
Electronics & Instrumetation from
Andhra University in 1992 and
completed his Ph.D in 2009 from JNTU in
the field of Antennas. He has joined JNT
University, Kakinada, Andhra Pradesh,
India, as Associate Professor of ECE in
June 2003. He got promoted as Professor
in ECE during Nov 2011. He has 20
publications in various International and
National Journals and conferences. His
areas of interest includes Antennas and
Bio-Medical Instrumentation”.
[4] S. Bi, C. K. Ho, and R. Zhang, “Wireless powered
communication: Opportunities and challenges,” IEEE
Commun. Mag., vol. 53, no. 4, pp. 117–125, Apr. 2015.
[5] W. Zhao and S. Wang, “Resource sharing scheme for
device-to-device communication underlaying cellular
networks,” IEEE Trans. Commun.,vol. 63, no. 12, pp. 4838–
4848, Dec. 2015.
[6] H. Wang, G. Ding, J. Wang, L. Wang, T. A. Tsiftsis, and P.
K. Sharma, “Resource allocation for energy harvestingpowered D2D communications underlaying cellular
networks,” in Proc. IEEE Int. Conf. Commun.,May 2017, pp.
1–6.
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